ISSN 1354–1013
http://www.blackwellpublishing.com/gcb
Global
Change
Biology
VOLUME 13
NUMBER 7
JULY 2007
• Daily ocean monitoring shows record warming of northern
European seas
• Development of stable isotope index to assess decadal-scale
vegetation change
• Responses of estuarine algal assemblages to enhanced
UV-B radiation
• Ecosystem carbon accretion after afforestation
Global Change Biology (2007) 13, 1335–1347, doi: 10.1111/j.1365-2486.2007.01360.x
Daily ocean monitoring since the 1860s shows record
warming of northern European seas
B R I A N R . M A C K E N Z I E * and D O R I S S C H I E D E K w
*Department of Marine Ecology and Aquaculture, Danish Institute for Fisheries Research, Technical University of Denmark,
Kavalergården 6, DK-2920 Charlottenlund, Denmark, wDepartment of Biological Oceanography, Baltic Sea Research Institute,
Seestrasse 15, D-18119 Rostock, Germany
Abstract
Ocean temperatures in most parts of the world are increasing and are expected to
continue to rise during the 21st century. A major challenge to ecologists and marine
resource managers is to understand and predict how these global changes will affect
species and ecosystems at local scales where temperature more directly affects biological
responses and species interactions. Here, we investigate historical variability in regional
sea surface temperature in two large heavily exploited marine ecosystems and compare
these variations with expected rates of temperature change for the 21st century. We use
four of the world’s longest calibrated daily time series to show that trends in surface
temperatures in the North and Baltic Seas now exceed those at any time since instrumented measurements began in 1861 and 1880. Temperatures in summer since 1985 have
increased at nearly triple the global warming rate, which is expected to occur during the
21st century and summer temperatures have risen two to five times faster than those in
other seasons. These warm temperatures and rates of change are due partly to an increase
in the frequency of extremely warm years. The recent warming event is exceeding the
ability of local species to adapt and is consequently leading to major changes in the
structure, function and services of these ecosystems.
Keywords: Baltic Sea, climate change, North Sea, surface temperature, warming
Received 29 May 2006; accepted 29 January 2007
Introduction
Knowledge of how local temperatures have varied in
the past, relative to biotic responses, and how temperatures will vary in the future, is fundamental for predicting biotic responses to temperature rise
(Drinkwater, 2006). However, due to sparse sampling
in the past, most data sets used to describe changes in
sea temperatures or marine biota are short (usually o50
years) or are averaged over large time and space scales
(e.g. annual means over entire ocean basins; IPCC, 2001;
Stenseth et al., 2004; Barnett et al., 2005). As a consequence, the historical biological and physical oceanographic context of the recent warm temperature period
is unclear, as is the potential for local biota to have
experienced similar conditions in the past, and therefore, to possess physiological and evolutionary mechanisms to facilitate adaptation to warmer temperatures
Correspondence: Brian R. Mackenzie, tel. 145 3396 3403,
fax 145 3396 3434, e-mail: brm@difres.dk
r 2007 The Authors
Journal compilation r 2007 Blackwell Publishing Ltd
(ICES, 2005; Somero, 2005; Pörtner & Knust, 2007).
Moreover, biota respond most directly to local conditions in their immediate habitats, rather than temperatures averaged over large space and time scales
(Walther et al., 2002; Somero, 2005). Interpreting and
predicting how individual populations and species in a
local ecosystem respond to temperature variations is,
therefore, more likely to be reliable when data are
scaled closely to their perceived environments and life
histories (e.g. a regional average by season for particular depth ranges).
Here, we investigate long-term variations in sea surface temperature (SST) in the North Sea, the Baltic Sea
and their transitional waters (Skagerrak, Kattegat,
Øresund and Belt Sea; Supplementary Fig. S1). Records
of daily direct temperature measurements using standardized and calibrated sampling techniques are available here since the mid–late 1800s (Sparre, 1984;
Ottersen et al., 2003; van Aken, 2003; MacKenzie &
Schiedek, 2007). These data are collected in programmes designed specifically for recording oceano1335
See Supplementary Fig. S1 for geographic locations and MacKenzie & Schiedek (2007) for further details. Websites containing updated data are listed with references in the
bibliography.
1861–2003
1867–2003
1880–1979
1880–1998
1870–2003
1870–2003
1914–2001
1905–2001
1904–2001
1923–2003
1911–2002
Daily monitoring
Daily monitoring
Daily monitoring
Daily monitoring
Opportunistic sampling
Opportunistic sampling
Opportunistic sampling
Opportunistic sampling
Opportunistic sampling
Opportunistic sampling
Opportunistic sampling
van Aken (2003)
Ottersen et al. (2003)
Sparre (1984)
Sparre (1984)
Rayner et al. (2003)
Rayner et al. (2003)
ICES www. ices.dk
ICES
ICES
ICES
ICES
52.9831N
58.3331N
57.7751N
55.3171N
511N–581N (54.51N)
54.01N–60.51N (56.751N)
551N–601N (57.51N)
551N–601N (57.51N)
551N–601N (581N)
55.9161N–54.751N (55.3331N)
551N–601N (56.251N)
Marsdiep, the Netherlands
Torungen, Norway
Skagens Reef, Denmark
Christians, Denmark
North Sea (HADISST1)
Baltic Sea (HADISST1)
Northeast North Sea-Skagerrak
Central North Sea
Northwest North Sea
Bornholm Basin
Kattegat–Øresund–Great Belt
4.751E
8.8831E
10.7251E
15.201E
21W–91E (3.51E)
14.51E–23.51E (19.01E)
51E–101E (7.51E)
01–51E (2.51E)
51W–01 (11W)
16.3331E–14.81E1 (15.5651E)
101E–151E (11.51E)
Data type
Source
Longitude
Latitude
The temperature data used in this study are fully
described elsewhere (MacKenzie & Schiedek, 2007),
and only a brief description is presented here. ‘Surface’
in this study refers to water in the upper 1 m of the
water column. The time scale and period of interest is
multidecadal during the past 1001 years.
Two main data sources were used in analyses. One
data source is based on daily long-term monitoring
programmes (Sparre, 1984; Ottersen et al., 2003; van
Aken, 2003; MacKenzie & Schiedek, 2007). Temperatures were recorded by professional staff of meteorological, naval or zoological/fisheries institutes as part
of oceanographic and meteorological monitoring pro-
Location
Data sets
Summary of sea surface temperature data used in this study
Materials and methods
Table 1
graphic conditions using standardized instruments at
permanent sampling locations (e.g. harbour wharves,
lightships, lighthouses; MacKenzie & Schiedek, 2007).
We supplemented these data with opportunistically
collected data contained in large international hydrographic databases (Table 1).
We have chosen to use SST data in this study because
it is an important indicator of the quality and variability
of habitats for marine species. A vast number of these
species from many trophic levels inhabit the surface
layer as fertilized eggs, juveniles or adults; the timing of
reproduction and the survival of early life history stages
of many of these species are coupled to interannual
variations in SST either directly via physiological effects, or indirectly via interactions with other species
(prey, predators, competitors). SST can be equal to
temperatures at much greater depths (e.g. 10 s of metres
as in tidally mixed areas such as most of the southern
North Sea and English Channel during the entire year;
during winter when storms mix water masses throughout the water column, and during summer months in
the upper 5–20 m mixed layer of the water column),
despite being measured in the upper metre of the water
column. Variations in SST are commonly used in climatological-physical oceanographic studies of air–sea heat
fluxes and heat budgets (IPCC, 2001; Rayner et al., 2003;
Döscher & Meier, 2004; Stenseth et al., 2004; Barnett
et al., 2005).
We wish to answer three questions related to the
long-term variability of SST: how warm are sea temperatures now compared with previously observed
maxima, how unique is the recent period of warming,
and have some seasons warmed at faster rates than
others? Answers to these questions are a pre-requisite
for estimating which species can adapt and which must
emigrate to other areas.
Period of measurements
1336 B . R . M A C K E N Z I E & D . S C H I E D E K
r 2007 The Authors
Journal compilation r 2007 Blackwell Publishing Ltd, Global Change Biology, 13, 1335–1347
WA R M I N G O F N O R T H E R N E U R O P E A N S E A S
grammes. All measurements were made using calibrated standard instruments (MacKenzie & Schiedek,
2007). Four sites, which had some of the longest sea
temperature monitoring records in Europe, were used
in this study: Marsdiep (the Netherlands), Torungen
(Norway), Skagen (Denmark) and Christians (Denmark). Locations and the years of measurements are
summarized in Table 1 and Supplementary Fig. S1. The
locations of the sites represent a diverse range of
hydrographic situations and are influenced by a variety
of large-scale hydrographic and climatological processes (Stenseth et al., 2004) including the North Atlantic
Oscillation, inflows of Atlantic water to the North Sea,
runoff of Baltic water to the North Sea and regional and
local climatic phenomena (Otto et al., 1990; Dippner,
1997; Helcom, 2002; MacKenzie & Schiedek, 2007). The
SST monitoring data from Marsdiep and Torungen are,
to the authors’ knowledge, the longest daily recorded,
calibrated sea-temperature series in the world.
The second data source we used were opportunistically collected data contained in large international
hydrographic databases. One database is held by the
International Council for the Exploration of the Sea
(ICES) and a second database (HADISST1) is maintained by the Hadley Centre of the UK Met Office
(Rayner et al., 2003). These databases use data from
heterogeneous sources including merchant vessels, research vessels, other sampling platforms and satellite
imagery, and the sampling coverage varies strongly in
time and space. In addition, the sampling methods
(depths, time of day, thermometers, etc.) differ (Rayner
et al., 2003; MacKenzie & Schiedek, 2007). Despite these
sampling differences these databases are widely used
within the oceanographic, climatological and meteorological communities. We have used these data in our
analyses to supplement and support results based on
the long-term monitoring data. Opportunistic temperature data were obtained from ICES and the Hadley
Centre for several regions of the North Sea, Baltic Sea
and their transitional waters (Supplementary Fig. S1).
The monitoring data have not been submitted to
either ICES or the Hadley Centre, hence comparisons
of these data with trends and results from ICES or the
HADISST1 data sets involve independent data sources.
We calculated seasonal and annual averages from the
monthly data available from monitoring programmes,
ICES and the Hadley Centre (MacKenzie & Schiedek,
2007). In total, 55 time series are used in this study (11
sites with four seasonal and one annual series per site).
All time series are available on the internet (http://
dx.doi.org/10.1016/j.jmarsys.2007.01.003).
We have shown elsewhere that there are high correlations in SST among the monitoring sites, and among
these sites and the HADISST1 data set (MacKenzie &
1337
Schiedek, 2007). These findings indicate that the singlesite temperature measurements associated with longterm monitoring data are representative of major temperature fluctuations over much larger spatial scales
(at least 1200 km) than those in the immediate vicinity
of where temperature measurements were made. The
spatial representativeness of the monitoring data is
likely due to the fact that several large-scale climatic
and hydrographic processes and phenomena (regional
cooling/warming, inflows of Atlantic water, the North
Atlantic Oscillation) affect thermal conditions over
large areas of northern Europe. The common regional
forcing of temperature in this area has been documented previously (Otto et al., 1990; Hurrell, 1995; Stenseth
et al., 2004; Sutton & Hodson, 2005).
Homogeneity of time series
Interpretations of long-term trends and variations in
time series assume that the time series themselves are
not subjected to sampling and instrument biases or that
such biases are small compared with real variations and
trends. The monitoring data are considered to be of
high quality because of consistent measuring techniques, use of calibrated instruments and employment of
professionally trained personnel (Fonselius, 2002;
Ottersen et al., 2003; van Aken, 2003; MacKenzie &
Schiedek, 2007). The Hadley HADISST1 data set has
been created by applying extensive processing to minimize the likelihood that potential sources of sampling
bias contaminate this data series (Rayner et al., 2003),
and is, therefore, one of the most important marine data
sets used in the climatological-oceanographic modelling communities. The ICES data are comparatively raw
because they do not represent a gridded, interpolated
data set similar to the HADISST1 data set. Although
we accommodated temporal variations in sampling
intensity when calculating averages from ICES data
(MacKenzie & Schiedek, 2007), we use these data only
to support results obtained with the monitoring and
Hadley data sets. Our major conclusions are based on
the monitoring and Hadley Centre data and are, therefore, very likely based on real variations and trends.
Data analyses
Analysis of trends and variability
Analyses were conducted to investigate long-term
variability and trends in both seasonal and annual data.
All seasonal and annual time series were plotted and
visually inspected to observe variations and trends.
Linear regression analyses were conducted to investigate whether overall increases or decreases in tempera-
r 2007 The Authors
Journal compilation r 2007 Blackwell Publishing Ltd, Global Change Biology, 13, 1335–1347
1338 B . R . M A C K E N Z I E & D . S C H I E D E K
ture occurred in individual time series. These analyses
used the raw seasonal or annual data.
To visualize and quantify multiyear variability in the
series, general additive models (GAMs) were used
(Hastie & Tibshirani, 1995). This modelling approach
is particularly useful for exploratory visualization of
major trends and variations in data sets, including time
series and spatial distributions, because they can model
nonlinearities using nonparametric smoothers. Examples of applications in the marine ecological literature
include analyses of fish feeding (Porter et al., 2005),
distributions (Swartzman et al., 1992; Begg & Marteinsdottir, 2002; Hedger et al., 2004) and population dynamics (Ciannelli et al., 2005). Unlike other time series
approaches [e.g. autoregressive and moving average
(ARIMA) models; Chatfield, 1989], GAMs can be applied to time series with missing observations and long
gaps. These characteristics were common in some of the
time series used in this study. In addition, and again
unlike ARIMA models, GAMs do not require autocorrelation and can therefore be an effective quantitative
modelling tool when autocorrelation is weak or absent,
as was also the case for the data series used in this study
(MacKenzie & Schiedek, 2007). Lastly, the fitted trends
can be derived using fully objective approaches. As a
result, the smoothed estimates yielded by GAMs do not
depend on the arbitrary user choice of a smoothing
window, as is the case when moving (or ‘running’)
averages are calculated and applied to time series.
GAMs were fitted to each time series using locally
weighted least squares regression (LOESS), an identity
link function and the Gaussian error distribution. The
amount of data used to fit the local regression for each
data point was objectively determined using a crossvalidation technique (Swartzman et al., 1992; SAS Inc.,
The SAS System for Data Analysis (Proc GAM), SAS
Corporation, Cary, NC, USA, 2000). Analyses were
conducted using SAS software (SAS Inc., 2000); the outputs included the GAM estimate of a best fit trend
through the data, 95% confidence limits for the trend
and a measure of residual deviance of the fitted model.
The significance of the fitted trend from GAM was
evaluated in two ways. First, if a horizontal line can
be drawn between the 95% confidence limits of the
fitted trend, then the model is insignificant (P40.05;
Swartzman et al., 1992). Second, significance was assessed quantitatively using the deviance estimates and
the pseudo-R2, which expresses the fraction of total
deviance (variance) explained by the model (Swartzman et al., 1992). The explained deviance depends
partly on the amount of smoothing (i.e. degrees of
freedom) used to fit the model and must be accommodated when calculating the pseudo-R2. The adjustment
of pseudo-R2 was carried out in a fashion similar to
adjusting the classical R2 for the number of independent
variables in a multiple regression model (Zar, 1999). The
adjustment used the following formula (Prof. E.
McKenzie, Department of Statistics and Modelling
Science, University of Strathclyde, Glasgow, Scotland,
personal communication):
pseudo-R2 ¼ 1 fðresidual device=ðN df smoothing 1ÞÞ
=ðdevice of null model=NÞg:
The deviance of the null hypothesis model is estimated by fitting the GAM to the overall mean of the
time series. Statistical significance of the pseudo-R
coefficients was assessed using t-tests, with degrees of
freedom given by Ndfsmoothing1. Residual variation
from the GAM fits was checked for autocorrelation for
lags between 0 and N/5 (Thompson & Page, 1989;
Pyper & Peterman, 1998) as a further evaluation of
goodness of fit. When present, significant autocorrelation would indicate temporal variability still remaining
in the series and a suboptimal fit of the model. Autocorrelation is considered significant if it exceeds the 95%
confidence limit for autocorrelation in a random time
series containing the same number of observations as a
given temperature time series (Chatfield, 1989).
The most recent warming period is of particular
interest because its influence on local species and
ecosystems has been described in many recent
studies (Reid et al., 2001; Beaugrand et al., 2002, 2004;
Brander et al., 2003; Beare et al., 2004; Genner et al., 2004;
MacKenzie & Köster, 2004; Perry et al., 2005). The
precise start date differs slightly from place to place
and among seasons. In addition, in some situations the
warming occurred after a relatively stable period when
temperatures only fluctuated by small amounts. Visual
inspection of the time series suggests that the most
intensive warming occurred after the mid-1980s in most
time series, which also corresponds to increasing evidence of a regime shift in the North Sea (Reid et al.,
2001, 2003). Rates of temperature warming (1C yr1)
were calculated for the period beginning in 1985 until
the next maximum temperature as described by GAM.
Because the GAM smoothes individual years’ data,
occasionally there were several consecutive years which
had the same maximum temperature. In these cases, the
warming period was defined as ending with the first of
a series of consecutive years having identical GAMfitted high temperatures.
Temperatures during the recent warm period (since
the mid–late 1980s; see ‘Results’) were compared with
previous periods of warm temperatures since 1861. This
comparison was carried out in two ways. First, metaanalysis was used to evaluate the hypothesis that recent
r 2007 The Authors
Journal compilation r 2007 Blackwell Publishing Ltd, Global Change Biology, 13, 1335–1347
WA R M I N G O F N O R T H E R N E U R O P E A N S E A S
temperatures have now exceeded historically observed
maxima. Within each time series, the warmest temperature during the last warm period was compared with
the maximum temperature observed previously anytime during the time series. This comparison was conducted for each seasonal and annual time series. The
frequencies of exceeding or not exceeding the historic
maxima were then compared using w2 analysis with a
random distribution of exceed events. The random
distribution (null hypothesis) assumed that half of the
time series would exceed their historical maximum.
GAM-fitted temperatures were used in these comparisons.
The second analysis quantified by how much the
recent period of warming exceeded peak temperatures
observed during previous warm periods. Temperature
differences between warm periods were compared
within and between time series for all seasons and for
annually averaged data. Because it is possible that the
magnitude of warming in summer may be larger in
absolute magnitude than the warming in winter, or
warming at some sites could be larger than at other
sites, all time series were first converted to standardized
temperatures using the following formula:
xi;stand: ¼
xi x
:
sx
This standardization only rescales the data to common units, does not alter the pattern of variability and
therefore facilitates comparisons among data sets.
GAMs were then fitted to the standardized time
series. The peak standardized temperatures in three
time periods were then extracted from each time series.
The time periods were defined based on visual inspection of the time series and on literature descriptions of
hydrographic variability in the area (Danielsson et al.,
1996; Helcom, 2002; ICES, 2004; Stenseth et al., 2004;
Sutton & Hodson, 2005), and are chosen to enclose peak
temperatures in different areas and seasons. The periods correspond to the following: pre-1900 (corresponding approximately to the warm period observed in the
1860s–1880s), 1925–1965 (corresponding approximately
to the warm period observed in the mid-1900s) and
1985–2005 (representing the most recent warm period).
Standardized temperature differences were then calculated between maximum temperatures observed during
the recent warm period and each of the previous warm
periods for each time series according to the equation
P5
ðtmax; last tmax; j Þ
DTst ¼ 1
;
N
where N 5 5 (four seasonal and one annually averaged
time series per site), tmax, last denotes the maximum
1339
temperature in the period 1985–2005 and tmax, j denotes
the maximum temperature during one of the previous j
time periods (1925–1965 or 1861–1899).
One-way ANOVA was used to evaluate the hypothesis
that differences in standardized temperature maxima
between warm periods were similar for different data
sets and geographic locations (Marsdiep, Torungen,
Skagen, Christians, entire North Sea and Baltic Sea).
Estimating probability of occurrence of extreme events
Global climate change models predict not only changes
in average conditions, but also an increase in the
frequency of extreme climatic events such as exceptionally warm temperatures (IPCC, 2001). We investigated
the hypothesis that the frequency of extreme sea temperature conditions has increased during the 120–140year period of our study.
We defined an extreme warm (cold) temperature
event as a season whose temperature was in the upper
(lower) 10% of the frequency distribution of all observations in a given seasonal time series. We then constructed chronologies of the extreme warm (cold)
years for the winter and summer seasons. Similar
chronologies were developed using annually averaged
data. These chronologies revealed when the extreme
events occurred and therefore allowed us to investigate
whether the frequencies of mild winters and hot summers have changed.
We calculated the decadal probability of extreme
temperature events in the winter, summer and for
annually averaged data. Probabilities were calculated
by summing the number of extreme events in each
decade observed in all six time series (i.e. the four
monitoring series and the two Hadley Centre series)
and dividing by the total number of years within the
decade for which temperature observations were available in the same six time series:
P6
j¼1 Ei
;
PðextremeÞk ¼ P6
j¼1 Nj
where Ei is the occurrence of an extreme temperature
year i within a given decade k, Nj is the number of years
within the same decade k for which seasonal or annual
temperature data are available and j represents the six
time series for a given season or annual mean.
Chi-square analysis was used to evaluate the significance of changes in the frequency of extreme and
nonextreme years across decades. To meet the requirement of a minimum of five expected events per cell in
the w2 analysis (Zar, 1999) in decades when too few
years were sampled, frequencies in the 1860s and 1870s
and in the 1990s and 2000s were summed. Frequencies
r 2007 The Authors
Journal compilation r 2007 Blackwell Publishing Ltd, Global Change Biology, 13, 1335–1347
4
2
0
2
0
6
4
6
Torungen, NO
0
1
2
3
4
5
Skagen, DK
1
2
3
4
5
Christiansø, DK
5
6
7
8
Hadley Ctr. North Sea
1
2
3
4
Hadley Ctr. Baltic Sea
16
18
15
16
17
18
16
18
15
16
17
16
18
8
9
10
7
8
9
10
10
11
8
9
7
Fig. 1 Time series of sea surface temperature (SST) measured at Marsdiep (Netherlands), Torungen (Norway), Skagens Reef (Denmark; intersection of the Skagerrak and Kattegat),
Christians (Denmark, southern Baltic Sea), the North Sea and the Baltic Sea. Panels from top to bottom represent the four seasons (January–February–March, April–May–June, July–
August–September and October–November–December) and annually averaged data. The thin solid line and dots represent observations. The thick solid line is the trend fitted by
general additive modelling (GAM; Hastie & Tibshirani, 1995). The dashed lines are the 95% confidence limits for the fitted GAM trend. See Table 2 for results of significance tests.
9
7
6
9
1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010
8
9
11
10
10
12
16
14
14
14
14
14
15
13
1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010
10
9
11
10
10
9
11
10
8
9
9
8
9
8
8
7
10
7
8
7
7
6
6
7
9
6
6
1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010
17
18
19
0
4
0
1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010
14
12
9
11
10
8
11
13
8
10
8
10
12
6
7
9
11
9
6
6
4
8
10
5
8
2
7
4
1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010
SST, Q1
SST, Q2
SST, Q3
SST, Q4
SST, Annual
Marsdiep, NL
1340 B . R . M A C K E N Z I E & D . S C H I E D E K
r 2007 The Authors
Journal compilation r 2007 Blackwell Publishing Ltd, Global Change Biology, 13, 1335–1347
WA R M I N G O F N O R T H E R N E U R O P E A N S E A S
1341
Table 2 Results of locally weighted least squares regression (LOESS) of variability in sea surface temperatures in the North and
Baltic Seas as represented by different data sets for the four seasons [January–February–March (JFM), April–May–June (AMJ),
July–August–September (JAS) and October–November–December (OND)] and annually averaged data
Area
JFM
AMJ
JAS.
OND
Annual
Marsdiep, NL
Torungen, NO
Skagen, DK
Christians, DK
North Sea (HADISST1)
Central Baltic Sea (HADISST1)
Northeast North Sea-Skagerrak
Central North Sea
Northwest North Sea
Kattegat-Øresund-Great Belt
Bornholm Basin
0.053, 143***
0.062, 132**
0.021, 116ns
0.032, 114ns
0.102, 136****
0.091, 136****
0.0913, 69*
0.001, 60ns
0.114, 50*
0.092, 59*
0.051, 38ns
0.205, 143***
0.203, 135***
0.054, 114**
0.043, 113*
0.264, 135****
0.151, 135****
0.022, 68ns
0.142, 70**
0.101, 61*
0.093, 63*
0.072, 43ns
0.255, 143***
0.369, 137***
0.199, 114***
0.145, 114****
0.368, 135****
0.214, 135****
0.155, 65**
0.185, 71***
0.213, 66***
0.224, 60***
0.051, 35ns
0.188, 143***
0.153, 136***
0.125, 115***
0.023, 117ns
0.196, 135****
0.112, 135***
0.193, 70***
0.236, 59**
0.447, 53***
0.002, 61ns
0.061, 33ns
0.234, 143***
0.314, 130***
0.104, 112***
0.063, 107*
0.314, 135****
0.234, 135****
0.053, 59ns
0.194, 46ns
0.112, 29ns
0.092, 48*
0.352, 16*
Results
Visual inspection and linear regression analysis of four
monitoring (Marsdiep, Torungen, Skagen and Christians) and two opportunistically collected (Hadley
Centre, UK; Rayner et al., 2003) data series show that
there is little evidence of a gradual linear increase or
decrease in temperature since the mid–late 1800s (Fig.
1). Linear regression explained no significant variability
in approximately half of the series (i.e. R2 5 0% in 26
and P40.05 in 29 of 55 series; Supplementary Table S1).
In contrast, nearly all of these data series display significant warming and cooling at shorter (multiannual)
time scales. In particular, approximately half time series
show a warm period in the mid-1900s and that warming has occurred during the last 10–15 years. This latest
warming is detectable in all seasons and in annually
B
B
A
B
B
0.09
(°C yr–1)
of extreme events were low in the 1860s and 2000s
because the number of years containing measurements
was low in these decades. We conducted a total of six w2
analyses as follows: one analysis was conducted each
for extremely cold and warm years for winter, summer
and annually averaged data.
We used the raw SST time series for identification of
extreme events instead of the GAM fits because the
GAM fits by their nature are smoothed versions of raw
data and because we were specifically investigating the
occurrence of large anomalies.
Rate of change of SST
Regressions were fitted using general additive modelling (GAM) with degrees of freedom (df) chosen objectively with a crossvalidation technique (Swartzman et al., 1992; SAS Inc., 2000]). Table entries are pseudo-R2 values which have been adjusted for the
number of df used to fit the models. Subscripts are the model df and the sample size N; superscripts are significance levels, where
ns, , ,
* ** ***, **** denote significance levels (respectively, P40.05, o0.05, o0.01, o0.001 and o0.0001). Entries with pseudo-R2 values
in italics (four time series) resulted in df between 23 and 31 and fitted models which overemphasized short-term variability. These
time series were then re-analysed using the mean df objectively selected by GAM for all other time series (i.e. df 5 4).
0.06
0.03
0.00
JFM AMJ JAS OND Ann.
Fig. 2 Seasonal and annual rates of temperature change
(mean 1 2 standard errors) between 1985 and the year with the
subsequent maximum temperature for six sea surface temperature (SST) time series in the North–Baltic Sea region. Data shown
are means across the six data sets for each season and for
annually averaged data. Means with the same letter at top of
panels are not significantly different (P40.05). Horizontal
dashed line is the consensus estimate of the expected global
annual rate of increase of air temperature during the 21st century
(Kerr, 2004).
averaged data. In addition to this warm period, the four
long-term monitoring time series indicate that there
was another warm period in the mid–late 1800s (ca.
1861 to mid-1880s; Fig. 1). Temperatures during this
first warm period were in many years similar to, and in
some individual years even higher than, those measured in the late 1990s and early 2000s.
Inspection of the GAM-derived fits shows that the
recent warming period in most time series is unprece-
r 2007 The Authors
Journal compilation r 2007 Blackwell Publishing Ltd, Global Change Biology, 13, 1335–1347
1342 B . R . M A C K E N Z I E & D . S C H I E D E K
Extremely cold
Extremely warm
0.8
0.3
Winter
Winter
0.6
0.2
0.4
0.1
0.2
0.0
1860
s
1870
s
1880
s
1890
s
1900
s
1910
s
1920
s
1930
s
1940
s
1950
s
1960
s
1970
s
1980
s
1990
s
2000
s
1860
s
1870
s
1880
s
1890
s
1900
s
1910
s
1920
s
1930
s
1940
s
1950
s
1960
s
1970
s
1980
s
1990
s
2000
s
0.0
0.2
0.1
0.0
Summer
0.6
0.4
0.2
0.0
1860
s
1870
s
1880
s
1890
s
1900
s
1910
s
1920
s
1930
s
1940
s
1950
s
1960
s
1970
s
1980
s
1990
s
2000
s
1860
s
1870
s
1880
s
1890
s
1900
s
1910
s
1920
s
1930
s
1940
s
1950
s
1960
s
1970
s
1980
s
1990
s
2000
s
0.3
Probability (T>90th percentile)
Probability (T<10th percentile)
0.8
Summer
0.8
0.3
Annual
Annual
0.6
0.2
0.4
0.1
0.2
0.0
1860
s
1870
s
1880
s
1890
s
1900
s
1910
s
1920
s
1930
s
1940
s
1950
s
1960
s
1970
s
1980
s
1990
s
2000
s
1860
s
1870
s
1880
s
1890
s
1900
s
1910
s
1920
s
1930
s
1940
s
1950
s
1960
s
1970
s
1980
s
1990
s
2000
s
0.0
Decade
Decade
Fig. 3 Probabilities of the occurrence of extremely cold (left panels) or warm (right panels) years per decade for winter, summer and
annually averaged sea surface temperature as estimated from six long-term data sets in the North and Baltic Seas. Extremely cold
and warm years are, respectively, those o10th and 490th percentiles of distributions. The differences in decadal probabilities within
each panel are statistically significant in all cases (w2 analysis: Po0.001). Decades with no bars are those in which no extreme events
occurred (i.e. decadal probability 5 0).
dented (Fig. 1, Table 2): 25 of the 30 monitoring and
Hadley Centre time series now have temperatures that
exceed all measurements since 1861. In addition, 22 of
the 25 time series based on opportunistic data held in
the ICES Hydrographic database are now warmer than
ever before (Supplementary Fig. S2). Both frequencies
are higher than expected by chance (w2 tests: Po0.005).
The GAMs effectively removed all significant time
trends as residuals showed no significant autocorrela-
tion for lags between 0 and N/5 (Supplementary
Fig. S3).
The magnitude and rate of warming for the monitoring and Hadley Centre data (i.e. the longest series with
most consistent temporal coverage) were calculated
for the period between 1985 and the year subsequently having the highest temperature (typically in
the early 2000s based on GAM fits; Fig. 1). Annual
mean temperatures rose on average 0.6 1C (standard
r 2007 The Authors
Journal compilation r 2007 Blackwell Publishing Ltd, Global Change Biology, 13, 1335–1347
Discussion
Species and marine ecosystems in the North and Baltic
Seas are becoming exposed to warm temperatures,
which are unprecedented in the history of instrumented
measurements in this region. Sea temperature trends in
all seasons and annual mean temperatures since the late
1980s have exceeded the measured maxima in 143 years
of daily observations. Moreover, the rate of sea temperature rise since 1985 is due at least partly to large
changes in the occurrence of extremely warm and cold
conditions during summer and winter. The frequency
of occurrence of extremely warm and cold years has,
A, B
2.5
B, C
C
B, C
A
1343
B, C
2.0
1.5
1.0
0.5
H
ad
le
le
y
y
Ba
N
or
lti
c
th
ø
C
H
ad
C
hr
is
tia
ns
Sk
ag
en
B
Sk
ag
en
C
2.5
ng
en
M
ar
sd
ie
p
To
ru
ng
en
0.0
B,C
A
B
2.0
1.5
1.0
0.5
le
y
H
ad
H
ad
le
y
N
Ba
lti
c
ø
ia
ns
is
t
hr
C
To
ru
di
ar
s
M
or
th
0.0
ep
Standardized temperature difference
(1985–2005 and 1861–1899; mean +2 SE)
error 5 0.06 1C) during this period (Fig. 2 and Supplementary Fig. S4). However, temperatures rose significantly faster and higher (41.4 1C) in the summer than in
other seasons and for annually averaged data (one-way
2
ANOVA: F 5 18.74, Po0.0001, R 5 0.75). There was no
significant difference among the six data sets in the
change in temperature (seasonal or annual averages)
between 1985 and the early 2000s (one-way ANOVA:
P40.50).
The probability of extremely warm winters, summers
and years has increased by two- to fourfold in the 1990s
and 2000s relative to the probability in nearly all previous decades; this change in frequency of extreme
events is statistically highly significant (Fig. 3). Since
1990 there has been a ca. 50% chance that any given
winter or summer has had a temperature in the warmest 10% of all measurements since at least 1880. Similarly, the probability of having extremely cold winters,
summers and years has decreased to o10% in these same
decades (Fig. 3).
The magnitude of the recent warming relative to
historical temperature maxima differs between data
sets: based on monitoring data, the latest warming
period has in most seasons recently exceeded all previous historical maxima by a few tenths of a degree. In
contrast, the Hadley Centre data series suggest that
recent temperatures are much warmer (ca. 1 1C) than
historical maxima in these time series (Figs 1 and 4).
One-way ANOVA showed that standardized temperature
differences between the recent and previous warm
periods within individual time series differed significantly depending on the time series (F 5 8.48,
Po0.0001, R2 5 64%; F 5 9.71, Po0.0001, R2 5 67% for
comparisons involving, respectively, the recent and
mid-1900s warm period, and the recent and late-1800s
warm period; Fig. 4). The difference in perception of
warming is most noticeable relative to the warm period
in the 1860s–1880s where the Hadley data sets suggest a
warming two to three times larger than that based on
the monitoring series (Fig. 4, lower panel).
Standardized temperature difference
(1985 –2005 and 1925 –1965; mean + 2 SE)
WA R M I N G O F N O R T H E R N E U R O P E A N S E A S
Fig. 4 Mean (1 two standard errors) standardized temperature
difference between maximum temperature observed between
1985–2005 and 1925–1965 (top panel) and 1985–2005 and 1861–
1900 (lower panel) for sea surface temperature measured at six
locations. Averages are based on seasonal and annually averaged
temperature series. Means with the same letter at top of panels
are not significantly different (P40.05; Student–Neuman–Keuls
test).
respectively, increased and decreased. This pattern is
consistent with changes in the frequency of extremes of
European air temperatures (Luterbacher et al., 2004;
Moberg et al., 2005). For example, several mild winters
in this same time period (Helcom, 2002; ICES, 2004;
Luterbacher et al., 2004; MacKenzie & Köster, 2004)
would have reduced the losses of heat remaining from
the previous summer across the air–sea interface that
usually occurs in winter (Otto et al., 1990). Major inflows
of warm Atlantic water to the North Sea in 1988 and
1998 have also increased North Sea temperatures (Reid
et al., 2001).
Summer warming rates since 1985 are nearly triple
those that could be expected on the basis of the emerging consensus view of the global warming of air
r 2007 The Authors
Journal compilation r 2007 Blackwell Publishing Ltd, Global Change Biology, 13, 1335–1347
1344 B . R . M A C K E N Z I E & D . S C H I E D E K
temperatures (Kerr, 2004), and assuming that SST responds in a similar magnitude as air temperature. It
must be emphasized, however, that the consensus view
is a global annual average for air temperatures, and that air
temperatures may not always track all scales of variability in SST. Data compiled here show significant
deviations from this view at seasonal and regional
scales, even though the annual average warming rate
is consistent with the consensus rate. The evidence from
the North and Baltic Seas shows that local biota are
therefore experiencing very different warming rates
from those expected from global annual averages. Summer warming rates also exceed warming rates during
other seasons. Similar seasonal differences in warming
were observed in the 1930s–1950s in the southern North
Sea (Becker & Kohnke, 1978) and elsewhere in the north
Atlantic (K. Drinkwater, Norwegian Institute of Marine
Research, personal communication, 2005).
Temperatures before the instrumental record are
available from paleo-climatic sources. For example,
temperatures in a central Swedish lake and the Skagerrak during the Holocene Optimum were 3–4 1C higher
than that at present (Mörner, 1980; Emeis et al., 2003).
The increase in annual (ca. 0.5 1C) and summer (ca.
1.5 1C) mean SST in the North Sea and Baltic Sea regions
since the mid-1980s, therefore, corresponds to ca. 12–
15% or 37–50% of the maximal warming seen in the last
10 000 years. The warm temperatures and rates of
change since the late 1980s to early-1990s are exceeding
the ranges of habitat preferences and scopes for thermal
physiological acclimation and evolutionary adaptation
in many local species. As a result, numerous zooplankton (Stenseth et al., 2004), benthic (Stenseth et al., 2004)
and fish (Genner et al., 2004; Stenseth et al., 2004; Perry
et al., 2005; Drinkwater, 2006) species in these ecosystems are responding to increasing temperatures by
relocating to cooler habitats. The warm temperatures
are also directly affecting life histories of diverse marine
taxa, such as the timing (Philippart et al., 2003; Edwards
& Richardson, 2004; Greve et al., 2005) and success of
reproduction (Thompson & Ollason, 2001; Philippart
et al., 2003; MacKenzie & Köster, 2004), and the links
between trophic levels (Philippart et al., 2003; Beaugrand et al., 2004; Edwards & Richardson, 2004). These
changes are analogous to responses associated with
earlier warm periods such as those during the warm
Atlantic period (7000-3900 BC; Enghoff et al., 2007) and
the mid-1900s (Drinkwater, 2006), and are consistent
with predictions based on thermal considerations of
biogeographical patterns (Walther et al., 2002) and physiological responses to temperature stress (Pörtner &
Knust, 2007).
The strength of this study is that the trends
and variations in SST are evident in data sets
designed intentionally to monitor sea temperature at
regular intervals using standardized sampling methods
over long periods of time. The fluctuations cannot be
attributed to potential biases associated with opportunistic sampling (e.g. sporadic temporal and spatial
coverage) or uncertainties associated with some proxy
indicators to represent true temperature (Moberg et al.,
2005).
Based on the different data sets, there is a difference
in perception of the amount of warming relative to
historical temperatures, and the implied temperature
stress to which living organisms are now being exposed. The difference is perhaps due to sparse data
coverage in the Hadley data for the specific locations
and years of this investigation. Nevertheless, the difference in perception of temperature change among data
sets affects our interpretation of the sensitivity of biota
to recent temperature changes, and our expectation of
the changes that can occur in future. Regional prognostic models of SST development during the next 80–100
years suggest that temperatures can be expected to be
2–4 1C higher in the latter decades of the 21st century
in both the North (Sheppard, 2004) and Baltic Seas
(Döscher & Meier, 2004), relative to the temperatures observed during 1961–1990. Late 21st century
temperatures may, therefore, be similar to those during
the Holocene Optimum (Mörner, 1980; Emeis et al.,
2003).
The recent ecological changes in these ecosystems,
while important and significant, may therefore be relatively minor compared with future ecological events,
particularly if further analyses show that the recent
ecological changes have been induced by temperatures
that exceed historical maxima by only few tenths of a
degree. Expectations of biodiversity and ecosystem
changes, which assume that sea temperature warming
rates will follow the global consensus view of warming
(Kerr, 2004), will likely underestimate the magnitude of
such changes because the global annual average dampens local, seasonal and multiannual variability to
which biota are more sensitive. Moreover, changes in
the seasonality of warming or the frequency of extremely warm and cold seasons or years, such as those
documented here using long-term data sets, increase
the probability that some biota will (or fail to) complete
their life histories or increase (or decrease) the number
of generations per year. For example, temperatures in
extreme years may be reaching thresholds for successful
completion of key physiological processes (e.g. gonadal
development, survival of early life-history stages), for
production of important prey species or becoming
suboptimal for feeding by predators. Mechanisms such
as these will also promote changes in distributions of
populations and species.
r 2007 The Authors
Journal compilation r 2007 Blackwell Publishing Ltd, Global Change Biology, 13, 1335–1347
WA R M I N G O F N O R T H E R N E U R O P E A N S E A S
Conclusions
The recent warm period, regardless of cause or the data
source (i.e. monitoring or opportunistic data) used for
its documentation, is unique in the past 120–140 years,
and is already having major ecological consequences.
As a result, this event and the prospects for continued
warming (Kerr, 2004; Barnett et al., 2005), even though
there are uncertainties in the rate and duration of future
warming (IPCC, 2001; Bryden et al., 2005), are challenging stakeholders (e.g. scientists, policymakers, the fishing industry) responsible for managing, exploiting and
conserving species and ecosystems (Root et al., 2003;
Garcia et al., 2006). Management frameworks and regulations for protecting marine species and ecosystems
will increasingly need to be designed to accommodate
such uncertainties and, in due course, to incorporate the
likelihood that long-term environmental change in a
given direction will occur. In some cases, this will mean
acknowledging that populations or ecosystems for long
periods of time (e.g. decades) could be on trajectories
towards new states.
Acknowledgements
This work was conducted within the projects CONWOY (Consequences of weather and climate changes for marine and freshwater ecosystems – conseptual and operational forecasting of
the aquatic environment; Danish National Science Foundation
SNF contract No. 2052-01-0034), the Census of Marine Life’s
History of Marine Animal Populations (CoML-HMAP), Global
Ocean Ecosystem Dynamics (GLOBEC) project, and the MarBEF
Network of Excellence ‘Marine Biodiversity and Ecosystem
Functioning’, which is funded by the Sustainable Development,
Global Change and Ecosystems Programme of the European
Community’s Sixth Framework Programme (contract no. GOCECT-2003-505446). This publication is contribution number MPS07008 of MarBEF. We thank the ICES Secretariat for providing
sea surface temperature data from the ICES Hydrographic
Database, J. Kennedy (Hadley Centre, UK Met Office) for providing SST from the HADISST1 data set, G. Ottersen (University
of Oslo) and K. Iden (Norwegian Meteorological Institute) for
SST for Torungen (Norway), E. Nielsen for assistance and
E. Buch, K. Brander, J. Cappelen, J. H. Christensen, H. Dooley,
T. Fenchel, H. Gislason, J. Kennedy, E. E. Nielsen, B. Poulsen,
H. van Aken and H. von Storch for discussions or reviews of
earlier versions of this manuscript.
References
Barnett TP, Pierce DW, AchutaRao KM, Gleckler PJ, Santer BD,
Gregory JM, Washington WM (2005) Penetration of humaninduced warming into the world’s oceans. Science, 309, 284–
287.
Beare DJ, Burns F, Greig T, Jones EC, Peach K, Keinzle M,
Mckenzie E, Reid DG (2004) Long-term increases in prevalence
of North Sea fishes having southern biogeographic affinities.
Marine Ecology Progress Series, 284, 269–278.
1345
Beaugrand G, Brander KM, Lindley JA, Souissi S, Reid PC (2004)
Plankton effect on cod recruitment in the North Sea. Nature,
426, 661–664.
Beaugrand G, Reid PC, Ibañez F, Lindley JA, Edwards M (2002)
Reorganization of North Atlantic marine copepod biodiversity
and climate. Science, 296, 1692–1694.
Becker G, Kohnke D (1978) Long-term variations of temperature
and salinity in the inner German Bight. Rapports et Procesverbaux de la Réunion du Conseil Internationale pour l’Exploration
de la Mer, 172, 335–344.
Begg GA, Marteinsdottir G (2002) Environmental and stock
effects on spatial distribution and abundance of mature cod
Gadus morhua. Marine Ecology Progress Series, 229, 245–262.
Brander KM, Blom G, Borges MF et al. (2003) Changes in fish
distribution in the eastern North Atlantic: are we seeing a
coherent response to changing temperature? ICES Marine
Science Symposium, 219, 260–273.
Bryden HL, Longworth HR, Cunningham SA (2005) Slowing
of the Atlantic meridional overturning circulation at 251N.
Nature, 438, 655–657.
Chatfield C (1989) The Analysis of Time Series. Chapman & Hall,
London.
Ciannelli L, Bailey KM, Chan K-S, Belgrano A, Stenseth NC
(2005) Climate change causing phase transitions of
walley pollock (Theragra chalcogramma) recruitment dynamics.
Proceedings of the Royal Society of London Series B, 272,
1735–1743.
Danielsson DS, Svendsen E, Ostrowski M (1996) Long-term
hydrographic variation in the Skagerrak based on the section
Torungen-Hirtshals. ICES Journal of Marine Science, 53, 917–925.
Dippner JW (1997) SST anomalies in the North Sea in relation to
the North Atlantic Oscillation and the influence on the theoretical spawning time of fish. German Journal of Hydrography,
49, 267–275.
Döscher R, Meier HEM (2004) Simulated sea surface temperature
and heat fluxes in different climates of the Baltic Sea. Ambio,
33, 242–248.
Drinkwater KF (2006) The regime shift of the 1920s and 1930s in
the North Atlantic. Progress in Oceanography, 68, 134–151.
Edwards M, Richardson AJ (2004) Impact of climate change
on marine pelagic phenology and trophic mismatch. Nature,
430, 881–884.
Emeis K-C, Struck U, Blanz T, Kohly A, Voss M (2003) Salinity
changes in the Baltic Sea (NW Europe) over the last 10,000
years. Holocene, 13, 411–421.
Enghoff IB, MacKenzie BR, Nielsen EE (2007) The Danish fish
fauna during the warm Atlantic period (ca. 7,000–3,900 BC):
forerunner of future changes? Fisheries Research, doi: 10.1016/
j.fishres.2007.03.004.
Fonselius S (2002) Hydrographical investigations in the Baltic
Sea area before the founding of ICES. ICES Marine Science
Symposium, 215, 138–147.
Garcia S, Boucher J, Cury P et al. (2006) Report of the debates and
proposed priority actions. Workshop 10, Paris Conference: Biodiversity, Science and Governance, 24–28 January 2005. Ministry of foreign affairs, Government of France, Paris, pp. 1–2.
Genner MJ, Sims DW, Wearmouth VJ, Southall EJ, Southward AJ,
Henderson PA, Hawkins SJ (2004) Regional climatic warming
r 2007 The Authors
Journal compilation r 2007 Blackwell Publishing Ltd, Global Change Biology, 13, 1335–1347
1346 B . R . M A C K E N Z I E & D . S C H I E D E K
drives long-term community changes of British marine fish.
Proceedings of the Royal Society of London Series B, 271, 655–661.
Greve W, Prinage S, Zidowitz H, Nast J, Reiners F (2005) On the
phenology of North Sea ichthyoplankton. ICES Journal Marine
Science, 62, 1216–1223.
Hastie TJ, Tibshirani RJ (1995) Generalized Additive Models. Chapman & Hall, London.
Hedger R, McKenzie E, Heath MR, Wright PJ, Scott B, Gallego A,
Andrews J (2004) Analysis of the spatial distributions of
mature cod (Gadus morhua) and haddock (Melanogrammus
aeglefinus) in the North Sea (1980–1999) using generalized
additive models. Fisheries Research, 70, 17–25.
Helcom (2002) Environment of the Baltic Sea area 1994–1998 (Background Document). Helsinki Commission Baltic Marine Environment Protection Commission, Helsinki, Finland, pp.
1–218.
Hurrell JW (1995) Decadal trends in the North Atlantic Oscillation: regional temperatures and precipitation. Science, 269,
676–679.
ICES (2004) The Annual ICES Ocean Climate Status Summary 2003/
2004 (eds Hughes SL, Lavin A), pp. 1–34. ICES, Copenhagen.
ICES (2005) Report of the ICES working group on genetics in
aquaculture and fisheries. ICES Council Meeting/F:01, pp. 1–51.
IPCC (2001) Climate Change 2001: The Scientific Basis. Cambridge
University Press, Cambridge, UK.
Kerr RA (2004) Three degrees of consensus. Science, 305,
932–934.
Luterbacher J, Dietrich D, Xoplaki E, Grosjean M, Wanner H
(2004) European seasonal and annual temperature variability,
trends and extremes since 1500. Science, 303, 1499–1503.
MacKenzie BR, Köster FW (2004) Fish production and climate:
sprat in the Baltic Sea. Ecology, 85, 784–794.
MacKenzie BR, Schiedek D (2007) Long-term spatial synchrony
of coastal and offshore sea surface temperatures in the North
Sea–Baltic Sea region. Journal of Marine Systems (accepted),
http://dx.doi.org/10.1016/j.jmarsys.2007.01.003
Moberg A, Sonechkin DM, Holmgren K, Datsenko NM, Karlén
W (2005) Highly variable Northern Hemisphere temperatures
reconstructed from low- and high-resolution proxy data.
Nature, 433, 613–617.
Mörner N-A (1980) A 10,700 years’ paleotemperature record
from Gotland and Pleistocene/Holocene boundary events in
Sweden. Boreas, 9, 283–287.
Ottersen G, Loeng H, Ådlandsvik B, Ingvaldsen R (2003) Temperature variability in the northeast Atlantic. ICES Marine
Science Symposium, 219, 86–94.
Otto L, Zimmerman JTF, Furnes GK, Mork M, Sætre R, Becker G
(1990) Review of the physical oceanography of the North Sea.
Netherlands Journal of Sea Research, 26, 161–238.
Perry AL, Low PJ, Ellis JR, Reynolds JD (2005) Climate change
and distribution shifts in marine fishes. Science, 308, 1912–
1915.
Philippart CJM, van Aken HM, Beukema JJ, Bos OG, Cadée GC,
Dekker R (2003) Climate-related changes in recruitment of
the bivalve Macoma balthica. Limnology and Oceanography, 48,
2171–2185.
Porter SM, Ciannelli L, Hillgruber N, Bailey KM, Chan K-S,
Canino MF, Haldorson LJ (2005) Environmental factors influ-
encing larval walleye pollock Theragra chalcogramma feeding in
Alaskan waters. Marine Ecology Progress Series, 302, 207–217.
Pörtner HO, Knust R (2007) Climate change affects marine fishes
through the oxygen limitation of thermal tolerance. Science,
315, 95–97.
Pyper BJ, Peterman RM (1998) Comparison of methods to
account for autocorrelation in correlation analyses of fish data.
Canadian Journal of Fisheries and Aquatic Science, 55, 2127–2140.
Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV,
Rowell DP (2003) Global analyses of sea surface temperature,
sea ice and night marine air temperature since the late nineteenth century. Journal of Geophysical Research, 108, 4407, doi:
10.1029/2002JD002670 (updates available at http://www.
hadobs.org/).
Reid PC, Edwards M, Beaugrand G, Skogen M, Stevens D (2003)
Periodic changes in the zooplankton of the North Sea during
the twentieth century linked to oceanic inflow. Fisheries Oceanography, 12, 260–269.
Reid PC, Holliday NP, Smyth TJ (2001) Pulses in eastern margin
current with higher temperatures and North Sea ecosystem
changes. Marine Ecology Progress Series, 215, 283–287.
Root TL, Price JT, Hall KR, Schneider SH, Rosenzweig C, Pounds
JA (2003) Fingerprints of global warming on wild animals and
plants. Nature, 421, 57–60.
Sheppard C (2004) Sea surface temperature 1871–2099 in 14 cells
around the United Kingdom. Marine Pollution Bulletin, 49,
12–16.
Somero GN (2005) Linking biogeography to physiology: evolutionary and acclimatory adjustments of thermal limits. Frontiers in Zoology, 2, 1.
Sparre A (1984) The climate of Denmark: summaries of observations from light vessels III. Sea temperature (updated data
supplied by John Cappelen, Danish Meteorological Institute,
Copenhagen, Denmark, personal communication). Danish
Meteorological Institute Climatological Papers, 10, 1–288.
Stenseth NC, Ottersen G, Hurrell JW, Belgrano A (2004) Marine
Ecosystems and Climate Variation. Oxford University Press,
Oxford.
Sutton RT, Hodson DLR (2005) Atlantic Ocean forcing of North
American and European summer climate. Science, 309, 115–
118.
Swartzman G, Huang C, Kaluzny S (1992) Spatial analysis of
Bering Sea groundfish survey data using generalized additive
models. Canadian Journal of Fisheries and Aquatic Science, 49,
1366–1378.
Thompson KR, Page FH (1989) Detecting synchrony of recruitment using short, autocorrelated time series. Canadian Journal
of Fisheries and Aquatic Science, 46, 1831–1838.
Thompson PM, Ollason JC (2001) Lagged effects of ocean climate
change on fulmar population dynamics. Nature, 413, 417–420.
van Aken HM (2003) One-hundred-and-forty years of daily
observations in a tidal inlet (Marsdiep). ICES Marine Science
Symposium, 219, 359–361 (updates available at http://
www.nioz.nl/nioz_nl/ccba2464ba7985d1eb1906b951b1c7f6.php).
Walther G-R, Post E, Convey P et al. (2002) Ecological responses
to recent climate change. Nature, 416, 389–395.
Zar JH (1999) Biostatistical Analysis. Prentice-Hall Inc., Upper
Saddle River, NJ, USA.
r 2007 The Authors
Journal compilation r 2007 Blackwell Publishing Ltd, Global Change Biology, 13, 1335–1347
WA R M I N G O F N O R T H E R N E U R O P E A N S E A S
Supplementary Material
The following supplementary material is available for
this article:
Fig. S1. Map of North Sea, Baltic Sea and transition
waters (Skagerrak, Kattegat, Øresund, and Belt Sea
(MacKenzie & Schiedek, 2007)). The sites where sea
surface temperature (SST) was recorded daily are
marked with stars. These locations were at Marsdiep
(Netherlands), Torungen (southern Norway), Skagens
Rev (northern Denmark) and Christans (Denmark;
southern Baltic Sea). The rectangular boxes on the map
are areas where ICES (diagonal line fill) and Hadley
Centre HADISST1 (cross-hatched fill) data (Rayner
et al., 2003) have been used as sources of sea surface
temperature data. Temperatures from the small boxed
area located between Sweden and Poland were also
obtained from ICES. See Table 1 for latitude and longitude coordinates for all sampling positions. Reprinted
from MacKenzie & Schiedek (2007) with permission
from Elsevier.
Fig. S2. Interannual variability in sea surface temperature in different areas of the North Sea, KattegatØresund-Great Belt, and Baltic Sea, based on data
contained in the ICES Hydrographic database. The
thin solid line and dots represents observations. The
thick solid line is the trend fitted by General Additive
Modelling (GAM (Hastie & Tibshirani, 1995)). The
dashed lines are the 95% confidence limits for the
fitted GAM trend. See Table 2 for results of significance
tests.
Fig. S3. Autocorrelation of residual variation in sea
surface temperature from GAM fitted models, where
residual 5 observedGAM-fit. measured at different
locations in the North Sea-Baltic Sea region. The
panels within a column represent (from top to bottom)
the four seasons and the annually-averaged residual
data from four monitoring sites (Marsdiep, Torungen,
Skagen, Christians) and Hadley Centre (Rayner et al.,
2003) data for the North Sea and Baltic Sea. Solid lines
1347
with dots: autocorrelation; dashed line: 95% confidence limits for autocorrelation for a random time
series (Chatfield, 1989). Autocorrelation of residuals
from GAM fits to ICES data were not calculated
because these series have many missing values and
long gaps (Supplementary Fig. S2), which prevent
reliable estimation of autocorrelation (Chatfield, 1989).
Fig. S4. Seasonal and annual temperature change
(mean 1 2 standard errors) between 1985 and the year
with the subsequent maximum temperature for six
SST datasets in the North-Baltic Sea region. Four
datasets are derived from dedicated long-term monitoring programmes (Marsdiep, Torungen, Skagen and
Christians (Sparre, 1984; Ottersen et al., 2003; van
Aken, 2003)), and two datasets (North Sea, central
Baltic Sea) are derived from opportunistic sampling
(Hadley Centre HADISST1 dataset (Rayner et al.,
2003)). The temperature increase in the summer was
significantly higher than increases in other seasons
and for annual data (1-way ANOVA and Student-Neumann-Keuls multiple comparison tests). Data shown
are means across the six datasets for each season and
for annually-averaged data. Means with the same
letter at top of panels are not significantly different
(P40.05).
Table S1. Results of linear regression analysis of the
hypothesis of an overall linear increase or decrease in
entire time series of sea surface temperature measured at
different locations in the North Sea, Baltic Sea and their
transitional waters. Data sources available in Table 1.
This material is available as part of the online article
from: http://www.blackwell-synergy.com/doi/abs/
10.1111/j.1365-2486.2007.01360.x (This link will take
you to the article abstract.)
Please note: Blackwell Publishing is not responsible
for the content or functionality of any supplementary
materials supplied by the authors. Any queries (other
than missing material) should be directed to the
corresponding author for the article.
r 2007 The Authors
Journal compilation r 2007 Blackwell Publishing Ltd, Global Change Biology, 13, 1335–1347
ISSN 1354–1013
http://www.blackwellpublishing.com/gcb
Global
Change
Biology
VOLUME 13
NUMBER 7
JULY 2007
• Daily ocean monitoring shows record warming of northern
European seas
• Development of stable isotope index to assess decadal-scale
vegetation change
• Responses of estuarine algal assemblages to enhanced
UV-B radiation
• Ecosystem carbon accretion after afforestation
Warming of northern European seas
B. R. MacKenzie and D. Schiedek
44
1
Daily ocean monitoring since the 1860s shows record warming of northern European seas (B.
2
R. MacKenzie and D. Schiedek)
3
4
(Web-based information):
5
6
7
Web Appendix Figure 1. Map of North Sea, Baltic Sea and transition waters (Skagerrak,
8
Kattegat, Øresund, and Belt Sea (MacKenzie and Schiedek, 2007)). The sites where sea
9
surface temperature was recorded daily are marked with stars. These locations were at
10
Marsdiep (Netherlands), Torungen (southern Norway), Skagens Rev (northern Denmark) and
11
Christansø (Denmark; southern Baltic Sea). The rectangular boxes on the map are areas
12
where ICES (diagonal line fill) and Hadley Centre HADISST1 (cross-hatched fill) data
13
(Rayner et al. 2003) have been used as sources of sea surface temperature data.
14
Temperatures from the small boxed area located between Sweden and Poland were also
15
obtained from ICES. See Table 1 for latitude and longitude coordinates for all sampling
16
positions. Reprinted from MacKenzie and Schiedek (2007) with permission from Elsevier.
17
18
Web Appendix Figure 2. Interannual variability in sea surface temperature in different areas
19
of the North Sea, Kattegat-Øresund-Great Belt, and Baltic Sea, based on data contained in the
20
ICES Hydrographic database. The thin solid line and dots represents observations. The thick
21
solid line is the trend fitted by General Additive Modelling (GAM (Hastie and Tibshirani,
22
1995)). The dashed lines are the 95% confidence limits for the fitted GAM trend. See Table
23
2 for results of significance tests.
24
Warming of northern European seas
B. R. MacKenzie and D. Schiedek
45
1
Web Appendix Figure 3. Autocorrelation of residual variation in sea surface temperature
2
from GAM fitted models, where residual = observed – GAM-fit. measured at different
3
locations in the North Sea-Baltic Sea region. The panels within a column represent (from top
4
to bottom) the four seasons and the annually-averaged residual data from four monitoring
5
sites (Marsdiep, Torungen, Skagen, Christiansø) and Hadley Centre (Rayner et al. 2003) data
6
for the North Sea and Baltic Sea. Solid lines with dots: autocorrelation; dashed line: 95%
7
confidence limts for autocorrelation for a random time series (Chatfield, 1989).
8
Autocorrelation of residuals from GAM fits to ICES data were not calculated because these
9
series have many missing values and long gaps (Web Appendix Figure 2), which prevent
10
reliable estimation of autocorrelation (Chatfield, 1989).
11
12
Web Appendix Figure 4. Seasonal and annual temperature change (mean + 2 standard errors)
13
between 1985 and the year with the subsequent maximum temperature for six SST datasets in
14
the North-Baltic Sea region. Four datasets are derived from dedicated long-term monitoring
15
programmes (Marsdiep, Torungen, Skagen and Christiansø (van Aken, 2003; Ottersen et al.
16
2003; Sparre, 1984a)), and two datasets (North Sea, central Baltic Sea) are derived from
17
opportunistic sampling (Hadley Centre HADISST1 dataset (Rayner et al. 2003)). The
18
temperature increase in the summer was significantly higher than increases in other seasons
19
and for annual data (1-way ANOVA and Student-Neumann-Keuls multiple comparison tests).
20
Data shown are means across the six datasets for each season and for annually-averaged data.
21
Means with the same letter at top of panels are not significantly different (P > 0.05).
22
Warming of northern European seas
B. R. MacKenzie and D. Schiedek
46
N
S
DK
UK
NL
D
Web Appendix Figure 1
P
Warming of northern European seas
B. R. MacKenzie and D. Schiedek
SST, Q4
SST, Q3
SST, Q2
SST, Q1
8
7
6
47
NW North Sea
Kattegat-ØresundGreat Belt
Bornholm Basin
7
6
4
4
6
4
2
2
5
0
5
2
0
1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010
10
11
10
11
10
10
10
9
8
9
9
9
8
8
6
8
8
7
7
7
4
1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010
15
18
16
18
18
14
17
15
16
16
16
13
14
15
13
12
14
14
14
12
11
1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010
11
11
12
12
11
11
10
10
9
9
1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010
11
SST, Annual
NE North Sea &
Skagerrak
Central North Sea
11
11
10
10
10
9
9
9
8
8
7
7
8
7
1860 1890 1920 1950 1980 2010
1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010
11
11
10
10
9
10
9
10
10
9
9
8
1860 1890 1920 1950 1980 2010
1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010
9
8
7
6
1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010
Warming of northern European seas
B. R. MacKenzie and D. Schiedek
Web Appendix Figure 2
48
Warming of northern European seas
B. R. MacKenzie and D. Schiedek
49
1
2
Torungen, N
Marsdiep, NL
Skagen, DK
0.50
0.25
0.00
-0.25
-0.50
0.50
0.25
0.00
-0.25
Autocorrelation
-0.50
0.50
0.25
0.00
-0.25
-0.50
0.50
0.25
0.00
-0.25
-0.50
0.50
0.25
0.00
-0.25
-0.50
0
6
12
18
Lag
3
4
5
Web Appendix Figure 3
24
30 0
6
12
18
Lag
24
30 0
6
12
18
Lag
24
30
Warming of northern European seas
B. R. MacKenzie and D. Schiedek
50
1
Christiansø, DK
HADISST1 North Sea
HADISST1 Baltic Sea
0.50
0.25
0.00
-0.25
-0.50
0.50
0.25
0.00
-0.25
Autocorrelation
-0.50
0.50
0.25
0.00
-0.25
-0.50
0.50
0.25
0.00
-0.25
-0.50
0.50
0.25
0.00
-0.25
-0.50
0
6
12
18
24
Lag
2
3
4
5
Web Appendix Figure 3 (cont’d.)
30 0
6
12
18
Lag
24
30
0
6
12
18
Lag
24
30
Warming of northern European seas
B. R. MacKenzie and D. Schiedek
51
1
2
3
4
5
6
0
Increase in SST ( C)
2.0
B
B
JFM
AMJ
A
B
B
1.5
1.0
0.5
0.0
JAS
Season
7
8
9
10
11
12
13
14
15
16
17
Web Appendix Figure 4
OND
Ann.
North Sea and Baltic Sea SST
B. R. MacKenzie and D. Schiedek
52
1
Web Appendix Table 1. Results of linear regression analysis of the hypothesis of an overall
2
linear increase or decrease in entire time series of sea surface temperature measured at
3
different locations in the North Sea, Baltic Sea and their transitional waters. Data sources
4
available in Table 1.
5
6
Area
Season
R2adj.
P
RMSE
N
Equation
Marsdiep,
NL
JFM
0
0.46
143
AMJ
0
0.34
143
JAS
0.04
0.0108
0.79
143 y = 0.0040*x+8.98
OND
0.05
0.0029
0.81
143 y = 0.0050*x-0.79
Annual
0.03
0.0176
0.67
143 y = 0.0032*x+4.03
JFM
0.06
0.0055
1.264
132 y = 0.0079*x-13.26
AMJ
0.17 < 0.0001
1.061
135 y = 0.012*x-18.96
JAS
0.15 < 0.0001
1.021
137 y = 0.011*x-5.48
OND
0.13 < 0.0001
0.811
136 y = 0.008*x-8.33
Annual
0.23 < 0.0001
0.683
130 y = 0.01*x-10.23
Torungen,
NO
Skagen, DK
Christiansø,
JFM
0
0.37
116
AMJ
0
0.97
114
JAS
0
0.19
114
OND
0.06
0.0059
Annual
0
0.36
112
JFM
0
0.22
114
0.79
115 y = 0.0058*x-3.03
North Sea and Baltic Sea SST
B. R. MacKenzie and D. Schiedek
53
DK
AMJ
0
0.14
113
JAS
0.02
0.048
OND
0
0.33
117
Annual
0
0.06
107
JFM
0.08
0.0006
0.66
136 y = 0.0051*x-4.02
AMJ
0.14 < 0.0001
0.59
135 y = 0.0061*x-2.53
0.09
0.0003
0.65
135 y = 0.0054*x+4.58
OND
0.12 < 0.0001
0.45
135 y = 0.0044*x+1.72
Annual
0.18 < 0.0001
0.42
135 y = 0.0052*x+0.01
JFM
0.09
0.0002
0.69
136 y = 0.0058*x-9.21
AMJ
0.13 < 0.0001
0.79
135 y = 0.0081*x-9.03
JAS
0.1 < 0.0001
0.94
135 y = 0.0084*x-0.77
0.0002
0.66
135 y = 0.0056*x-3.54
0.19 < 0.0001
0.56
135 y = 0.0070x-5.61
1.19
114 y = 0.0065*x+3.02
North Sea
(HADISST1)
JAS
Central
Baltic Sea
(HADISST1)
OND
Annual
0.09
Northeast
North SeaSkagerrak
JFM
0
0.24
69
AMJ
0
0.43
68
JAS
0
0.1
65
OND
0
0.98
70
Annual
0
0.8
59
North Sea and Baltic Sea SST
B. R. MacKenzie and D. Schiedek
54
Central
North Sea
JFM
0
0.59
60
AMJ
0.11
0.0033
JAS
0
0.0946
71
OND
0
0.23
59
Annual
0
0.09
46
JFM
0
0.07
50
AMJ
0.10
0.0061
JAS
0
0.27
OND
0.22
0.0002
0.44
53 y = 0.0084*x-6.31
Annual
0.11
0.0446
0.44
29 y = 0.0068*x-3.77
JFM
0
0.24
59
AMJ
0
0.28
63
JAS
0
0.85
60
OND
0
0.61
61
Annual
0
0.18
48
JFM
0
0.09
38
AMJ
0.07
0.0438
JAS
0
0.31
35
OND
0
0.54
33
Annual
0
0.26
16
0.6
70 y = 0.0080*x-6.97
Northwest
North Sea
0.54
61 y = 0.0070*x-5.17
66
KattegatØresundGreat Belt
Bornholm
Basin
1
1.36
45 y = 0.021*x-35.21